Efficient Crowd Density Estimation: Fusion of Faster CNN and Vision Transformer

Efficient Crowd Density Estimation: Fusion of Faster CNN and Vision Transformer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Efficient Crowd Density Estimation: Fusion of Faster CNN and Vision Transformer Hemant Kushwaha, Sanjai Kumar Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4630456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate crowd density estimation in densely populated areas is essential for applications such as urban planning, security, and event management. Traditional methods often fall short in terms of accuracy and efficiency, especially when handling large datasets and real-time demands. This study introduces an efficient approach to crowd density estimation by integrating a rapid Convolutional Neural Network (CNN) with a Vision Transformer (ViT) architecture.Our method capitalizes on the strengths of both models to achieve enhanced performance. The fast CNN effectively captures local features and spatial details, while the Vision Transformer excels at identifying long-range dependencies and global context. By fusing these models, we create a synergistic effect that significantly improves crowd density estimation accuracy.We validate our approach through experiments on several benchmark datasets, covering both indoor and outdoor scenes with varying crowd densities. The results indicate that our method surpasses existing techniques in terms of accuracy while also maintaining high computational efficiency. We further demonstrate the scalability of our approach by testing its performance on large-scale datasets, confirming its suitability for real-world applications. Additionally, we explore the interpretability of the fused model, showing how the combination of CNN and Vision Transformer features enhances crowd density estimation. In summary, our proposed fusion method offers a promising solution for efficient and accurate crowd density estimation, with broad implications for practical applications in crowd analysis and management. Crowd density estimation CNN ViT Real time analysis Feature Fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4630456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327464530,"identity":"b147f371-0c6a-4279-8dfe-ecf2bdcaafdb","order_by":0,"name":"Hemant Kushwaha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYHACNgjFw8DADKQSGNgbgJSBBWEtPHAtPAdAWiRI0SKRAOLj1qI7+wDbg485dnn2PGeMPxe22eXxz3x+dcOPAgkG/vbuBGxazM4lsBvO3JZczMPbYyY9sy25WOJ2TtnNHqDDJM6c3YBVyxkGNmnebcyJPfw8ZswgRsPtnLQbPEAtBhK5uLX83VYP0mL8mRfImH/zTNrNP4S0MG47nNjD22MAtO5w4oYb7Mdu47eFsU2yd9vxxJ4zx8qkef8dT9x4JofttoyBBA9uvzAfk/i5rTqxvSd582eeM9WJ844ff3bzzR8bOf72XqxaGBgYG9BFeAzAJHbl2AH7A1JUj4JRMApGwfAHAAqAYh1sbRu4AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hemant","middleName":"","lastName":"Kushwaha","suffix":""},{"id":327464533,"identity":"36dcf7f7-d141-4a17-91db-14c3b5898703","order_by":1,"name":"Sanjai Kumar Gupta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sanjai","middleName":"Kumar","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2024-06-24 13:16:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4630456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4630456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60856776,"identity":"a5550ecd-3c1a-45ae-a2d6-ce738c75ef00","added_by":"auto","created_at":"2024-07-22 23:10:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575872,"visible":true,"origin":"","legend":"","description":"","filename":"EfficientCrowdDensityEstimationFusionofFasterCNNandVisionTransformer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4630456/v1_covered_2c5990fc-f5a7-4d26-99d8-b2083bd9f7ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Crowd Density Estimation: Fusion of Faster CNN and Vision Transformer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Crowd density estimation, CNN, ViT, Real time analysis, Feature Fusion","lastPublishedDoi":"10.21203/rs.3.rs-4630456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4630456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate crowd density estimation in densely populated areas is essential for applications such as urban planning, security, and event management. 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